A timely product recommendation can lead shoppers to choose one product over another. Look to your own experience for proof.
Have you ever made a selection at a local store, based on a product recommendation from the owner or a sales clerk? Has a product recommendation from a friend or family member ever been the deciding factor in your choice of which product to purchase?
Celebrity endorsements (paid or unpaid), advice given by a current user, “Best 10” lists on blogs – all of these mentions, and more, leverage the power of product recommendations.
As an ecommerce manager, though, you don’t have to wait for someone else to recommend a product to your customers. You can make product recommendations while the prospect is in the process of shopping on your website. Personalized product recommendations can work to improve the user experience as well as conversion rate of your site.
In this article, we’ll reveal some of the most effective ways we’ve found to deliver product recommendations to your customers. The information we provide here will help you sell more products, more often.
How Do Ecommerce Product Recommendation Engines Work?
While it’s possible to manually implement rudimentary “also-liked” recommendations on your ecommerce site, product recommendations best practices call for the deployment of a ‘product recommendations engine’.
There are three basic approaches used to configure the underlying algorithm:
- The content-based filtering method analyzes customer data on the likes and dislikes of each user (cookies allow tracking over multiple visits), then makes recommendations based on the browsing history of that user. The idea behind content-based filtering is that if you enjoy a certain item, you’ll likely also enjoy a similar item. An example of a content-based filtering system would be if you were listening to Pandora and consistently ‘liked’ downtempo jazz music. The filtering system would take that information and begin recommending similar music to you based on the songs you preferred.
- The collaborative-filtering method incorporates data from users who have purchased similar products, then combines that information to make decisions about recommendations. The advantage to this filtering method is that it is capable of making complex recommendations on items such as music or movies without having to ‘understand’ what the item is. This method of filtering operates under the assumption that users will prefer recommendations that are based on purchases they made in the past. Here’s an example: If customer A likes a specific line of products that customer B also likes (assuming they have similar interests), then collaborate-filtering would assume that the customer A would like other products that customer B purchased and vice versa.
- A hybrid method combines the content-based and collaborative-based methods to incorporate group decisions, but focus the output based on attributes of a specific visitor. An example of a hybrid filtering system would be how Spotify curates their personalized ‘Discover Weekly’ playlists. If you’ve ever listened to a personalized Spotify playlist, it’s shocking how accurately they’re able to recommend songs based on what you like. The secret behind how they pull this off is through a complex hybrid filtering system that aggregates data on your listening habits as well as similar users’ listening habits, to create a playlist of unique songs that align with your personal taste.
All three methods use machine-learning algorithms to fuel the process and provide personalized product recommendations. While the mathematical principles behind each are elaborate and complicated, the application to your online store doesn’t have to be overwhelming. If you have questions, ask in the comments section (below), and you’ll get an answer from a staff member at The Good.
What Are the Benefits of a Product Recommendations Engine?
Is the product recommendations process really worth the trouble? Isn’t the incorporation of machine learning a bit beyond the scope of all but the largest ecommerce websites?
Those are the types of questions we often hear from clients. There are times when it seems the high-tech movement is going too far, and machine-learning algorithms are a prime example of that complaint.
Given the potential benefits, though, the argument often settles itself. When a tool proves itself sufficiently valuable, the question moves from “Why?” to “How?”.
- Research conducted by Barilliance in 2018 concluded that product recommendations accounted for up to 31 percent of ecommerce revenues. On average, customers saw 12 percent of their overall purchases coming from products that were recommended to them.
- A Salesforce study of product recommendations concluded that visits where the shopper clicked a recommendation comprise just 7 percent of total site traffic, but make up 24 percent of orders and 26 percent of revenue.
- The conversion rate for visitors clicking on product recommendations was found to be 5.5x higher than for visitors who didn’t click.
- A Gartner study predicts engines that gauge and react to customer intent will be capable of boosting ecommerce profits as much as 15 percent by 2020.
- As online shoppers become more used to personalization, they equate it with professionalism – meaning your site needs to bump up to keep up.
- An Accenture report says personalization increases the likelihood of a prospect purchasing from you by 75 percent.
Studies increasingly show the value of product recommendations and the critical role they play in personalization strategies. Recommendations not only lift conversion rates, they help deliver improved user experience to keep visitors coming back and can boost the average order value.
Once an ecommerce manager is convinced of the benefits of a product recommendation engine, the next step is to determine product recommendation best practices and configure the product recommendation algorithm accordingly.
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21 Best Practice Tips for Ecommerce Product Recommendations – The List
Your ecommerce site will lend itself to some of the following tips, but not to others. We’ll list the kinds of tactics we’ve seen our clients effectively implement. You choose the ones that seem most applicable to your own business.
- Displaying a list of suggested products based on the visitor’s browsing history (“Recommended for you”) is an often-used and effective type of product recommendation – to add deeper impact, personalize with the shopper’s name.
- Use “Frequently bought together” recommendations to increase average order value (AOV).
- Use product recommendation engines to personalize your email campaigns. Integrate product recommendations into your email marketing strategy by sending personalized emails to your customers with product recommendations based on their recent purchase history.
- “Featured recommendation” and “Recently viewed” suggestions can introduce shoppers to items they wouldn’t have thought about searching for.
- Providing access to the shopper’s browsing history can help save sales that may have been lost had the customer not been able to relocate an item earlier viewed.
- Show “Related to items you’ve viewed” suggestions on product pages to help encourage users to add additional items to their cart.
- “Customers who bought [this item] also bought [that item]” recommendations provide social proof and peer-generated recommendations of relevant products the user may be interested in.
- Alert viewers of products that have been updated by generating “There is a newer version of this item” notices.
- Personalize recommendations by showing items related to previous purchases (“Since you already own this, you may also want this”).
- Feature best-selling items for each brand for indirect social proof and as a way of adding confidence to the purchase. Recommending best-selling products on the homepage has shown to be a highly-effective tactic for hooking your users attention as soon as they reach your site.
- Generate product bundles (items frequently purchased together) and offer a special discount for purchasing the group.
- Don’t limit references to best-selling items to one product or brand. show best-sellers across entire product categories.
- Make sure all recommendations are relevant and timely. they should also be informed by returns and reviews.
- Adjust your recommendations to keep popular products highlighted and to provide additional viewing opportunities for lower-selling items (20 percent of your items will provide 80 percent of your sales).
- Show highest rated items in product recommendations. Try injecting some social proof into your product recommendations by displaying items that have the highest customer reviews.
- Know your visitors. the more personalization you can add, the better your results.
- Provide product recommendations when items added to the cart require accessories (fishing reels need fishing line, flashlights need batteries, shoes often require socks).
- Use product recommendations for moving the buyer up to a more fully-featured version of the one currently being browsed (upselling).
- Use product recommendations to remind the shopper about upcoming holidays or other special events.
- Never stop A/B testing. your product recommendations engine isn’t a set it and forget it function; add it to your testing regimen for conversion optimization.
- Offer recommended product pairings on the shopping cart page. Before your customers move into the checkout process, you have one last opportunity to present them with product recommendations. If you opt to use this tactic, make sure the products you’re offering don’t distract users from completing the purchase. Cross-selling related items that compliment the items already in their cart is the best approach here.
The #1 Reason Why You Should Get Started with Product Recommendations
Strategic marketing plans vary from company to company. Tactics that fit one business often wouldn’t be a wise move for another business. Implementing a product recommendation engine, however, is something every ecommerce manager should seriously consider.
Here’s why: your competitors will soon enough. And the advantage gained from applying product recommendation examples, like the ones given above, is significant.
If you’re looking for a simple and highly-effective way to improve personalization for your ecommerce store, product recommendation engines may be an investment worth making. Beyond simply getting your customers to add more items to their cart, you’re providing them with a better overall shopping experience through customized recommendations for products that they otherwise might not have found on their own.
Here at The Good, we’re committed to working with brands both large and small to help improve user experiences and drive conversion rates upward. If you’re seeking actionable ways to improve the shopping experience of your site, sign-up for a free landing page assessment where we’ll take a close look at your site and identify ways to begin optimizing it for more conversions.
About the Author
Jon MacDonald is founder and President of The Good, a conversion rate optimization firm that has achieved results for some of the largest online brands including Adobe, Nike, Xerox, Verizon, Intel and more. Jon regularly contributes content on conversion optimization to publications like Entrepreneur and Inc. He knows how to get visitors to take action.